Enhancing Fault Isolation and Detection for Electric Powertrains of UAVs

The system revolves around a Communications Remote Monitoring Panel (CRMP), which interfaces with multiple radios operating under different communication protocols. The CRMP intelligently identifies each radio’s communication specification, such as analog, digital, or proprietary protocols, and dynamically adjusts to ensure the correct protocol is used for proper communication. This capability allows for seamless interaction between otherwise incompatible systems. Once connected, the CRMP transmits queries to assess the status of each connected radio to retrieve metrics including operational status, voltage, power status, and internal temperature. The CRMP can also remotely switch radios on or off, or flag problems for maintenance crews. The system supports real-time data acquisition, cross-protocol translation, and scalable network management. Whether deployed at a single control center or across multiple geographically dispersed facilities, the system improves visibility into the health of communication networks while minimizing the need for on-site technicians. Data trends may also inform replacement equipment acquisitions. Its modular, scalable architecture enables simultaneous monitoring of multiple radios, making it ideal for expanding networks or integrating legacy and next-gen systems.

This critical safety tool can be used for a wider variety of aircraft, including general aviation, helicopters, and unmanned aerial vehicles (UAVs) while also improving performance in the fighter aircraft currently using this type of system. Demonstrations/Testing This improved approach to ground collision avoidance has been demonstrated on both small UAVs and a Cirrus SR22 while running the technology on a mobile device. These tests were performed to the prove feasibility of the app-based implementation of this technology. The testing also characterized the flight dynamics of the avoidance maneuvers for each platform, evaluated collision avoidance protection, and analyzed nuisance potential (i.e., the tendency to issue false warnings when the pilot does not consider ground impact to be imminent). Armstrong's Work Toward an Automated Collision Avoidance System Controlled flight into terrain (CFIT) remains a leading cause of fatalities in aviation, resulting in roughly 100 deaths each year in the United States alone. Although warning systems have virtually eliminated CFIT for large commercial air carriers, the problem still remains for fighter aircraft, helicopters, and GAA. Innovations developed at NASAs Armstrong Flight Research Center are laying the foundation for a collision avoidance system that would automatically take control of an aircraft that is in danger of crashing into the ground and fly it—and the people inside—to safety. The technology relies on a navigation system to position the aircraft over a digital terrain elevation data base, algorithms to determine the potential and imminence of a collision, and an autopilot to avoid the potential collision. The system is designed not only to provide nuisance-free warnings to the pilot but also to take over when a pilot is disoriented or unable to control the aircraft. The payoff from implementing the system, designed to operate with minimal modifications on a variety of aircraft, including military jets, UAVs, and GAA, could be billions of dollars and hundreds of lives and aircraft saved. Furthermore, the technology has the potential to be applied beyond aviation and could be adapted for use in any vehicle that has to avoid a collision threat, including aerospace satellites, automobiles, scientific research vehicles, and marine charting systems.

Every 12 seconds, the Dynamic Weather Route (DWR) automation system computes and analyzes trajectories for en-route flights. DWR first identifies flights that could save 5 or more flying minutes (wind-corrected) by flying direct to a downstream return fix on their current flight plan. Eligible return fixes are limited so as not to take flights too far off their current route or interfere with arrival routings near the destination airport. Using the direct route as a reference route, DWR inserts up to two auxiliary waypoints as needed to find a minimum-delay reroute that avoids the weather and returns the flight to its planned route at the downstream fix. If a reroute is found that can save 5 minutes or more relative to the current flight plan, the flight is posted to a list displayed to the airline or FAA user. Auxiliary waypoints are defined using fix-radial-distance format, and a snap to nearby named fix option is available for todays voice-based communications. Users may also adjust the alert criteria, nominally set to 5 minutes, based on their workload and desired potential savings for their flights. A graphical user interface enables visualization of proposed routes on a traffic display and modification, if necessary, using point, click, and drag inputs. If needed, users can adjust the reroute parameters including the downstream return fix, any inserted auxiliary waypoints, and the maneuver start point. Reroute metrics, including flying time savings (or delay) relative to the current flight plan, proximity to current and forecast weather, downstream sector congestion, traffic conflicts, and conflicts with special use airspace are all updated dynamically as the user modifies a proposed route.

The digital twin NAS simulator provides a complete digital copy of the individual systems that comprise the NAS to allow for the creation of offline simulations to test proposed changes to one or more individual systems based on actual historical data from the NAS or on real-time data from the NAS. The NAS is composed of a collection of systems, including source systems such as weather stations from various locations or airports, which are used by other systems such as individual aircraft flight data and airline operators. Other systems may include management systems such as the FAA, air traffic control centers, and flight traffic monitors. Operational data from each of these systems may be archived by a central information sharing platform such as the System Wide Information Management (SWIM) Program operated by the FAA. The digital twin NAS simulator can access archived SWIM data to create a digital twin NAS system to provide a virtual environment that may operate in real-time alongside the actual NAS, with the digital twin receiving live data updates from the actual NAS. A dedicated application programming interface (API) is used to facilitate communication between various distributed external components and the testbed. The testbed receives NAS data during a test and feeds the data to the simulation manager for use with a digital twin of the NAS system. The result is a virtual environment that is an exact twin of the actual operational system and is able to function identically to the actual NAS system because it is based on and uses the same data archived from the actual NAS system. A primary function of the virtual twin NAS is that it will allow for changes to one or more systems to be simulated against the archived NAS data and subsequently allow for a comparison between the simulated results and the actual results from the operational system. The digital twin simulator may also function in a distributed network environment, allowing for simulations of different elements to run simultaneously, which speeds up and improves the testing and evaluation of proposed changes.

The MAchine learning ESTimations for uRban Operations (MAESTRO) system is a novel approach that couples commodity sensors with advanced algorithms to provide real-time onboard local wind and kinematics estimations to a vehicle's guidance and navigation system. Sensors and computations are integrated in a novel way to predict local winds and promote safe operations in dynamic urban regions where Global Positioning System/Global Navigation Satellite System (GPS/GNSS) and other network communications may be unavailable or are difficult to obtain when surrounded by tall buildings due to multi-path reflections and signal diffusion. The system can be implemented onboard an Unmanned Aerial Systems (UAS) and once airborne, the system does not require communication with an external data source or the GPS/GNSS. Estimations of the local winds (speed and direction) are created using inputs from onboard sensors that scan the local building environment. This information can then be used by the onboard guidance and navigation system to determine safe and energy-efficient trajectories for operations in urban and suburban settings. The technology is robust to dynamic environments, input noise, missing data, and other uncertainties, and has been demonstrated successfully in lab experiments and computer simulations.